Autonomous vehicle object detection method and apparatus

ABSTRACT

An object detection method and apparatus for a vehicle in a confined space are provided. An object detection method for a vehicle traveling in a confined space, includes determining a first beam pattern and a second beam pattern based on geometric information of the confined space, detecting first candidate objects based on a first transmission signal emitted to form the first beam pattern using at least one antenna, detecting second candidate objects based on a second transmission signal emitted to form the second beam pattern using the at least one antenna, detecting at least one clutter object based on the first candidate objects and the second candidate objects, and detecting a target object based on the at least one clutter object.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 USC § 119(a) of KoreanPatent Application No. 10-2017-0157298, filed on Nov. 23, 2017, in theKorean Intellectual Property Office, the entire disclosure of which isincorporated herein by reference for all purposes.

BACKGROUND 1. Field

The following description relates to an object detection method andapparatus for an autonomous vehicle, and more particularly, to an objectdetection method and apparatus for an autonomous vehicle that travels ina confined space.

2. Description of Related Art

An autonomous vehicle emits a signal from within or outside theautonomous vehicle, receives the reflected emitted signal, and analyzesthe received signal to detect a range, angle, and/or velocity of objectswithin the vicinity of the autonomous vehicle. The distance between theobject and the autonomous vehicle is calculated based on the amount oftime it took for the emitted signal to return, and an angle of theobject relative to the vehicle being calculated based on the angle andintensity at which the reflected signal is received.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In one general aspect, an object detection method for a vehicletraveling in a confined space, includes, determining a first beampattern and a second beam pattern based on geometric information of theconfined space, detecting first candidate objects based on a firsttransmission signal emitted to form the first beam pattern using atleast one antenna, detecting second candidate objects based on a secondtransmission signal emitted to form the second beam pattern using the atleast one antenna, detecting at least one clutter object based on thefirst candidate objects and the second candidate objects, and detectinga target object based on the at least one clutter object.

The detecting of the first candidate objects may include: emitting thefirst transmission signal to form the first beam pattern using the atleast one antenna; receiving a first receive signal corresponding to thefirst transmission signal; detecting objects for the first receivesignal based on the first receive signal; and classifying the objectsfor the first receive signal into a plurality of preset regions ofinterest (ROIs) based on a distance from the vehicle, the plurality ofROI comprising a target region, and the first candidate objects beingincluded in the target region.

The detecting of the second candidate objects may include: emitting thesecond transmission signal to form the second beam pattern using the atleast one antenna; receiving a second receive signal corresponding tothe second transmission signal; detecting objects for the second receivesignal based on the second receive signal; and classifying the objectsfor the second receive signal into the plurality of ROIs based on thedistance from the vehicle, and the target region comprising the secondcandidate objects.

The detecting of the at least one clutter object may include detectingthe at least one clutter object based on a radar cross-section (RCS) ofeach of the first candidate objects and an RCS of each of the secondcandidate objects.

The detecting of the target object may include, in response to twoclutter objects being detected, detecting an object located between thetwo clutter objects as the target object.

The determining of the first beam pattern and the second beam patternmay include: setting a plurality of ROIs in the confined space based onthe geometric information and a location of the vehicle; and determiningthe first beam pattern and the second beam pattern that cover theplurality of ROIs.

The object detection method may further include: acquiring the geometricinformation based on a location of the vehicle.

The object detection method may further include: generating a drivingroute for the vehicle within the confined space based on the at leastone clutter object and the target object; and controlling the vehiclebased on the driving route.

A non-transitory computer-readable storage medium storing instructionsthat, when executed by a processor, may cause the processor to performthe object detection method.

In another general aspect, an object detection apparatus included in avehicle traveling in a confined space, includes a processor configuredto determine a first beam pattern and a second beam pattern based ongeometric information about the confined space, detect first candidateobjects based on a first transmission signal emitted to form the firstbeam pattern using at least one antenna, detect second candidate objectsbased on a second transmission signal emitted to form the second beampattern using the at least one antenna, detect at least one clutterobject based on the first candidate objects and the second candidateobjects, and detect a target object based on the at least one clutterobject.

The object detection apparatus may further include a memory configuredto store instructions, wherein the processor is further configured toexecute the instructions to configure the processor to determine thefirst beam pattern and the second beam pattern based on geometricinformation about the confined space, detect the first candidate objectsbased on the first transmission signal emitted to form the first beampattern using the at least one antenna, detect the second candidateobjects based on the second transmission signal emitted to form thesecond beam pattern using the at least one antenna, detect the at leastone clutter object based on the first candidate objects and the secondcandidate objects, and detect the target object based on the at leastone clutter object.

To perform the detecting of the first candidate objects, the processormay be configured to: emit the first transmission signal to form thefirst beam pattern using the at least one antenna; receive a firstreceive signal corresponding to the first transmission signal; detectobjects for the first receive signal based on the first receive signal;and classify the objects for the first receive signal into a pluralityof preset regions of interest (ROIs) based on a distance from thevehicle, the plurality of ROIs comprising a target region, and the firstcandidate objects being included in the target region.

To perform the detecting of the second candidate objects, the processormay be configured to: emit the second transmission signal to form thesecond beam pattern using the at least one antenna; receive a secondreceive signal corresponding to the second transmission signal; detectobjects for the second receive signal based on the second receivesignal; and classify the objects for the second receive signal into theplurality of ROIs based on the distance from the vehicle, and the targetregion comprising the second candidate objects.

To perform the detecting of the at least one clutter object, theprocessor may be configured to detect the at least one clutter objectbased on a radar cross-section (RCS) of each of the first candidateobjects and an RCS of each of the second candidate objects.

To perform the detecting of the target object, the processor may beconfigured to, in response to two clutter objects being detected, detectan object located between the two clutter objects as the target object.

To perform the determining of the first beam pattern and the second beampattern, the processor may be configured to: set a plurality of ROIs inthe confined space based on the geometric information and a location ofthe vehicle; and determine the first beam pattern and the second beampattern that cover the plurality of ROIs.

The program may be further executed to acquire the geometric informationbased on a location of the vehicle.

The program may be further executed to: generate a driving route for thevehicle within the confined space based on the at least one clutterobject and the target object; and control the vehicle based on thedriving route.

The confined space may include at least one of a tunnel and a road witha barricade.

In another general aspect, an object detection method for a vehicletraveling in a confined space, method includes: acquiring geometricinformation of the confined space; setting a plurality of regions ofinterest (ROIs) in the confined space based on the geometric informationand a location of the vehicle; determining a field of view (FoV) foreach of the plurality of ROIs; emitting a transmission signal based onthe determined FoV; and detecting an object based on a receive signalcorresponding to the transmission signal.

In another general aspect, a processor implemented object detectionmethod for a vehicle traveling in a confined space, includes:determining a plurality of regions of interest (ROIs) based on geometricinformation of the confined space; determining beam patterns based onthe plurality of ROIs; detecting corresponding candidate objects basedon transmission signals emitted to form the beam patterns using at leastone antenna; detecting clutter objects based on currently detectedcandidate objects and subsequently detected candidate objects; anddetecting a target object based on the clutter objects.

The determining of the plurality of ROIs may be further based a currentdirection and location of the vehicle.

The clutter objects may be detected based on a radar cross-section (RCS)of the candidate objects.

The method may further include: generating a driving route for thevehicle within the confined space based on the clutter objects and thetarget object; and controlling the vehicle based on the driving route.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a vehicle traveling in a tunnel.

FIG. 2 illustrates an example of a configuration of an object detectionapparatus.

FIG. 3 is a flowchart illustrating an example of an object detectionmethod.

FIG. 4 is a flowchart illustrating an example of determining a firstbeam pattern and a second beam pattern.

FIG. 5 illustrates an example of a plurality of regions of interest(ROIs) in a confined space.

FIG. 6 illustrates an example of a field of view (FoV) for each of aplurality of ROIs.

FIG. 7 illustrates an example of a first beam pattern and a second beampattern.

FIG. 8 is a flowchart illustrating an example of detecting objects for afirst receive signal.

FIG. 9 is a flowchart illustrating an example of detecting objects for asecond receive signal.

FIG. 10 illustrates an example of a detected first candidate object anda detected second candidate object.

FIG. 11 illustrates an example of a radar cross-section (RCS) value of afirst candidate object, an RCS value of a second candidate object and adifference between RCS values.

FIG. 12 is a flowchart illustrating an example of controlling a vehiclebased on a driving route.

FIG. 13 is a flowchart illustrating an example of an object detectionmethod.

Throughout the drawings and the detailed description, the same referencenumerals refer to the same elements. The drawings may not be to scale,and the relative size, proportions, and depiction of elements in thedrawings may be exaggerated for clarity, illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after an understanding of thedisclosure of this application. For example, the sequences of operationsdescribed herein are merely examples, and are not limited to those setforth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order. Also,descriptions of features that are known may be omitted for increasedclarity and conciseness.

As used herein, the term “and/or” includes any one and any combinationof any two or more of the associated listed items.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided merelyto illustrate some of the many possible ways of implementing themethods, apparatuses, and/or systems described herein that will beapparent after an understanding of the disclosure of this application.

The terminology used herein is for the purpose of describing particularexamples only and is not intended to be limiting of the presentinventive concept. As used herein, the singular forms “a,” “an” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. It will be further understood thatthe terms “include” and/or “have,” when used in this specification,specify the presence of stated features, integers, operations, elements,components or combinations thereof, but do not preclude the presence oraddition of one or more other features, integers, operations, elements,components, and/or groups thereof.

Unless otherwise defined, all terms including technical and scientificterms used herein have the same meaning as commonly understood by one ofordinary skill in the art and after an understanding of the disclosureto which these examples belong. It will be further understood thatterms, such as those defined in commonly used dictionaries, should beinterpreted as having a meaning that is consistent with their meaning inthe context of the relevant art and will not be interpreted in anidealized or overly formal sense unless expressly so defined herein.

In the description of examples, detailed description of well-knownrelated structures or functions will be omitted when it is deemed thatsuch description could cause ambiguous interpretation of the presentdisclosure.

FIG. 1 illustrates an example of a vehicle 100 that travels in a tunnel.

The vehicle 100 emits a signal from within or outside the vehicle 100,receives the reflected emitted signal, and analyzes the received signalto detect objects within the vicinity of the vehicle 100. The vehicle100 is, for example, an autonomous vehicle. The vehicle 100 includes oneor more antennas, transmitters and/or receivers. The plurality oftransmitters may be located at different orientation angles to transmitsignals. The plurality of receivers may be located at differentorientation angles to receive reflected signals.

When the vehicle 100 passes through a confined space, for example, atunnel, the wall 110 of the tunnel is detected as an object orobstruction based on a reflected signal. When the wall 110 is detectedas an object or obstruction, the wall 110 boundary is used to generateor set the driving route of the vehicle 100.

The wall 110 or object reflects the signal emitted from the vehicle 100.The direction and location of an object detected based on a signalreflected by the wall 110 is different from the direction and locationof the real object reflecting the signal. In an example, an object maybe located inside a tunnel but the location of the object calculatedbased on the signal reflected by the wall 110 may be incorrectlydetermined to be outside the tunnel. In this example, the objectdetermined to be in a wrong location is referred to as a “ghost object.”Due to ghost objects, the location of real objects may be ambiguous ornot detected.

When a field of view (FoV) for a signal emitted from the vehicle 100 iswider than a width of the tunnel, a ghost object may be created anddetected in the signal reflected by the wall 110. Thus, when it isdetermined that the vehicle 100 is traveling in a confined space, thevehicle 100 may detect a real object in the confined space by adjustingthe FoV for the emitted signal.

FIG. 2 illustrates an example of a configuration of an object detectionapparatus 200.

Referring to FIG. 2, the object detection apparatus 200 includes acommunicator 210, a processor 220, and a memory 230, for example. Theobject detection apparatus 200 may be included in the vehicle 100 ofFIG. 1.

The communicator 210 is connected to the processor 220 and the memory230, and is configured to transmit and receive data to and from theprocessor 220 and the memory 230. The communicator 210 may be connectedto an external device, and configured to transmit and receive data toand from the external device. In the following description, theexpression “transmitting and receiving ‘A’” refers to the transmittingand the receiving of data or information representing “A”.

The communicator 210 is implemented, for example, as a circuitry in theobject detection apparatus 200. In an example, the communicator 210 mayinclude an internal bus and an external bus. In another example, thecommunicator 210 may be a device configured to connect the objectdetection apparatus 200 to an external device, for example, an interfacedevice. The communicator 210 receives data from the external device andtransmits data to the processor 220 and the memory 230.

The processor 220 is configured to process data received by thecommunicator 210 and data stored in the memory 230. The term“processor,” as used herein, may be a hardware-implemented dataprocessing device having a circuit that is physically structured toexecute desired operations discussed hereinafter. In another example,the hardware implemented data processing device may be configured toimplement one or more desired operations through the execution of codeor instructions included in a program, e.g., stored in memory of theobject detection apparatus 200. The hardware-implemented data processingdevice may include, but is not limited to, for example, amicroprocessor, a central processing unit (CPU), a processor core, amulti-core processor, a multiprocessor, an application-specificintegrated circuit (ASIC), and a field-programmable gate array (FPGA).

Thus, in one or more examples, the processor 220 executes acomputer-readable code (for example, software) stored in a memory (forexample, the memory 230), and executes instructions caused by theprocessor 220.

The memory 230 is configured to store data received by the communicator210 and data processed by the processor 220. As noted above, in anexample, the memory 230 stores a program. The stored program is coded todetect an object and is a set of syntax executable by the processor 220.

The memory 230 includes, for example, any one or any combination of anytwo or more of a volatile memory, a nonvolatile memory, a random accessmemory (RAM), a flash memory, a hard disk drive and an optical discdrive.

The memory 230 stores an instruction set (for example, software) tooperate the object detection apparatus 200. The instruction set tooperate the object detection apparatus 200 is executed by the processor220.

The communicator 210, the processor 220 and the memory 230 will befurther described below with reference to FIGS. 3 through 12.

FIG. 3 is a flowchart illustrating an example of an object detectionmethod.

Operations 310 through 360 of FIG. 3 are performed by, for example, theobject detection apparatus 200 of FIG. 2, noting that the examples arenot limited thereto.

In operation 310, the processor 220 acquires geometric information abouta confined space. For example, the location of the vehicle 100 isdetermined using a global positioning system (GPS) as to whether thevehicle 100 is approaching a confined space or the vehicle 100 is in aconfined space. In an example, when the vehicle 100 is determined toapproach a confined space, the processor 220 loads map data stored inthe memory 230 to acquire the geometric information about the confinedspace. The acquired geometric information is, for example, a floor planof the confined space.

In operation 320, the processor 220 determines a first beam pattern anda second beam pattern based on the geometric information. The beam maybe a beam of radio waves and/or optical beam. The first beam pattern isa signal pattern of signals that are emitted first, and the second beampattern is a signal pattern of signals that are emitted subsequently.The first beam pattern and the second beam pattern are configured tocover the confined space. For example, the first beam pattern and thesecond beam pattern are used to amplify a reflected or received signalof an object located at a long distance by focusing signals to theconfined space.

The vehicle 100 includes one or more sensors that may include a radarand/or a LIDAR configured to emit an RF and/or optical signal to detectan object that may be based on data from the sensors and GPS data. Forexample, a radar example may include an antenna configured to work witha plurality of transmitters, a plurality of receivers, and/ortransceivers. The transmitters emit RF signals in a first beam patternand a second beam pattern, and the plurality of receivers receivesignals reflected by objects. Examples of the first beam pattern and thesecond beam pattern will be further described below with reference toFIGS. 4 through 7.

In operation 330, the processor 220 detects first candidate objectsbased on a first transmission signal emitted to form the first beampattern. An example of detecting first candidate objects will be furtherdescribed below with reference to FIG. 8.

In operation 340, the processor 220 detects second candidate objectsbased on a second transmission signal emitted to form the second beampattern. An example of detecting second candidate objects will befurther described below with reference to FIG. 9.

In operation 350, the processor 220 detects a clutter object based onthe first candidate objects and the second candidate objects. Clutter isunwanted echoes detected in a reflected signal. In an example, when theconfined space is a tunnel, a wall of the tunnel is detected as aclutter object. In another example, when the confined space is a roadwith a barricade (or a guardrail) in a center lane, the barricade isdetected as a clutter object. An example of detecting a clutter objectwill be further described below with reference to FIG. 11.

In operation 360, the processor 220 detects a target object based on theclutter object. For example, when the confined space is a tunnel, anobject located in the tunnel is detected as a target object. The targetobject is located between both side walls of the tunnel and any detectedobject that is not located between the side walls of the tunnel isdetermined to be clutter, which may include a ghost object.

FIG. 4 is a flowchart illustrating an example of determining a firstbeam pattern and a second beam pattern.

Referring to FIG. 4, operation 320 of FIG. 3 includes operations 410 and420.

In operation 410, the processor 220 sets a plurality of regions ofinterest (ROIs) in the confined space based on the geometricinformation, and a direction and a location of the vehicle 100. Thedirection and the location of the vehicle 100 are acquired based ontraveling information that may be acquired from the vehicle's GPS. Theplurality of ROIs are set based on a distance from the vehicle 100. Anexample of a plurality of ROIs will be further described below withreference to FIG. 5.

In operation 420, the processor 220 determines the first beam patternand the second beam pattern that cover the plurality of ROIs. Theprocessor 220 controls a plurality of transmitters so that the firstbeam pattern and the second beam pattern are formed by emitted signals.

FIG. 5 illustrates an example of a plurality of ROIs in a confinedspace.

Referring to FIG. 5, a plurality of ROIs, for example, a first ROI 521through a sixth ROI 526, are set in the confined space based ongeometric information about the confined space, a direction of thevehicle 100 and a location of the vehicle 100. The geometric informationrepresents, for example, side walls 500 of a tunnel. Regions in which aFoV 510 of the vehicle 100 overlaps with a space between the walls 500are set as the first ROI 521 through the sixth ROI 526. Each of thefirst ROI 521 through the sixth ROI 526 is set based on a distance fromthe vehicle 100.

FIG. 6 illustrates an example of a FoV for each of a plurality of ROIs.

FoVs are calculated for each of a plurality of ROIs. For example,referring to FIGS. 5 and 6, a first FoV 610 for the first ROI 521, asecond FoV 620 for a second ROI 522, a third FoV 630 for a third ROI523, a fourth FoV 640 for a fourth ROI 524, a fifth FoV 650 for a fifthROI 525, and a sixth FoV 660 for the sixth ROI 526 are calculated.

FIG. 7 illustrates an example of a first beam pattern 710 and a secondbeam pattern 720.

The first beam pattern 710 and the second beam pattern 720 arecalculated to cover the first FoV 610 through the sixth FoV 660 of FIG.6. For example, the first beam pattern 710 is a signal pattern ofsignals emitted first, and a first direction 712 is a central axis ofthe first beam pattern 710. The second beam pattern 720 is a signalpattern of signals emitted at a subsequent time to the first beampattern 710, and a second direction 714 is a central axis of the secondbeam pattern 720.

FIG. 8 is a flowchart illustrating an example of detecting objects for afirst receive signal.

Referring to FIG. 8, operation 330 of FIG. 3 includes operations 810through 840.

In operation 810, the processor 220 emits the first transmission signalto form the first beam pattern using a plurality of antennas. Forexample, the processor 220 calculates an orientation angle forcorresponding transmitters for the antennas to form the first beampattern. Each of the plurality of transmitters emits a signal at a setcalculated orientation angle. Signals emitted by the plurality oftransmitters may have different frequencies and/or different phases. Inthis example, the emitted signals are first transmission signals, and afirst beam pattern is formed by the first transmission signals.

In operation 820, the processor 220 receives, through correspondingreceivers for the antennas, a first receive signal corresponding to thefirst transmission signal. The first receive signal includes a pluralityof received signals. For example, the processor 220 determines whetherthe first receive signal is a reflected signal for the firsttransmission signal, based on a phase and/or a frequency of the firstreceive signal.

In operation 830, the processor 220 detects objects in the first receivesignal based on the first receive signal. For example, the processor 220calculates a distance (range) to an object and a direction (angle) ofthe object based on the phase and/or the frequency of the first receivesignal.

In operation 840, the processor 220 classifies the objects for the firstreceive signal into a plurality of ROIs (for example, the first ROI 521through the sixth ROI 526 of FIG. 5) based on a distance from thevehicle.

First candidate objects are detected for each of the plurality of ROIs.For example, an object classified as the first ROI 521 is detected as afirst candidate object in the first ROI 521, and an object classified asthe second ROI 522 is detected as a first candidate object in the secondROI 522.

FIG. 9 is a flowchart illustrating an example of detecting objects for asecond receive signal.

Referring to FIG. 9, operation 340 of FIG. 3 includes operations 910through 940.

In operation 910, the processor 220 emits the second transmission signalto form the second beam pattern using a plurality of antennas. Forexample, the processor 220 calculates an orientation angle forcorresponding transmitters for the antennas to form the second beampattern. Each of the plurality of transmitters emits a signal at a setcalculated orientation angle. Signals emitted by the plurality oftransmitters may have different frequencies and/or different phases. Inthis example, the emitted signals are second transmission signals, and asecond beam pattern is formed by the second transmission signals.

In operation 920, the processor 220 receives, through correspondingreceivers for the antennas, a second receive signal corresponding to thesecond transmission signal. The second receive signal includes aplurality of received signals. For example, the processor 220 determineswhether the second receive signal is a reflected signal for the secondtransmission signal, based on a phase and/or a frequency of the secondreceive signal.

In operation 930, the processor 220 detects objects for the secondreceive signal based on the second receive signal. For example, theprocessor 220 calculates a distance (range) to an object and a direction(angle) of the object based on the phase and/or the frequency of thesecond receive signal.

In operation 940, the processor 220 classifies the objects for thesecond receive signal into a plurality of ROIs (for example, the firstROI 521 through the sixth ROI 526 of FIG. 5) based on a distance fromthe vehicle.

Second candidate objects are detected for each of the plurality of ROIs.For example, an object classified as the first ROI 521 is detected as asecond candidate object in the first ROI 521, and an object classifiedas the second ROI 522 is detected as a second candidate object in thesecond ROI 522.

FIG. 10 illustrates an example of a detected first candidate object anda detected second candidate object.

Referring to a left portion of FIG. 10, objects 1011, 1012 and 1013 aredetected based on a first transmission signal emitted to form the firstbeam pattern 710. The objects 1011 through 1013 are detected based on anorientation angle at which each of a plurality of transmitters emits asignal of the first transmission signal, an angle at which each of aplurality of receivers receives a signal of the first transmissionsignal, and an intensity of the received first transmission signal. Theobjects 1011 through 1013 are located at angles θ1, θ2 and θ3, withrespect to a front side of the vehicle 100, respectively.

A first radar cross-section (RCS) of each of the detected objects 1011through 1013 is calculated based on a signal received by each of theplurality of receivers. The objects 1011 through 1013 are classified asthe same ROI and are included as first candidate objects in the ROI(classified as first candidate objects in the same ROI). The objects1011 through 1013 are classified as a predetermined ROI as shown in FIG.10, however, there is no limitation thereto. For example, objects foreach of a plurality of ROIs may be simultaneously classified.

After the first transmission signal is emitted to form the first beampattern 710, a second transmission signal is emitted to form the secondbeam pattern 720. Referring to a right portion of FIG. 10, objects 1011,1012 and 1013 are detected based on the second transmission signalemitted to form the second beam pattern 720. The objects 1011 through1013 are detected based on an orientation angle at which each of aplurality of transmitters emits a signal of the second transmissionsignal, an angle at which each of a plurality of receivers receives asignal of the emitted second transmission signal, and an intensity ofthe received second transmission signal. A second RCS of each of theobjects 1011 through 1013 is calculated. The objects 1011 through 1013are classified as the same ROI and are included as second candidateobjects in the ROI (classified as second candidate objects in the sameROI). For example, when an object classified as a predetermined ROI bythe first beam pattern 710 and an object classified as a predeterminedROI by the second beam pattern 720 are located within a preset distancerange and a preset angle range, the objects are recognized as the sameobject.

A clutter object and a target object for a predetermined ROI aredetected based on the first RCS and the second RCS. An example ofdetecting a clutter object and a target object based on a first RCS anda second RCS will be further described below with reference to FIG. 11.

FIG. 11 illustrates an example of an RCS value of a first candidateobject, an RCS value of a second candidate object, and a differencebetween RCS values.

A left graph 1110 of FIG. 11 illustrates a first RCS trajectory 1120 anda second RCS trajectory 1130. The first RCS trajectory 1120 representsRCS values of first candidate objects, and the second RCS trajectory1130 represents RCS values of second candidate objects. A right graph1140 of FIG. 11 illustrates differences between the RCS values of thefirst candidate objects and the RCS values of the second candidateobjects.

Referring to FIGS. 7 and 10, in an example in which the firsttransmission signal is emitted to form the first beam pattern 710, whenan object is located closer to the first direction 712 that is thecentral axis of the first beam pattern 710, an RCS value reflected bythe object increases. RCS is a measure of a target's ability to reflectradar signals in the direction of the radar receiver. In this example,when the object is located further away from the first direction 712,the RCS value decreases. The first RCS trajectory 1120 is calculated fora predetermined ROI based on a first receive signal. Due to the object1012 being closer to the first direction 712 than the objects 1011 and1013, an RCS value 1122 of the object 1012 is greater than an RCS value1121 of the object 1011 and an RCS value 1123 of the object 1013. Anobject with an RCS value calculated to differ from the first RCStrajectory 1120 by at least a preset ratio among objects classified as apredetermined ROI is highly likely to be a ghost object, and accordinglyis excluded from first candidate objects.

Similarly to the first RCS trajectory 1120, the second RCS trajectory1130 is calculated for a predetermined ROI based on a second receivesignal. Referring to FIGS. 7 and 10, because the object 1013 is closerto the second direction 714 than the objects 1011 and 1012, an RCS value1133 of the object 1013 is greater than an RCS value 1131 of the object1011 and an RCS value 1132 of the object 1012. An object with an RCSvalue calculated to differ from the second RCS trajectory 1130 by atleast a preset ratio among objects classified as a predetermined ROI ishighly likely to be a ghost object, and accordingly is excluded fromsecond candidate objects.

Differences 1141, 1142 and 1143 between the RCS values are calculatedbased on the first RCS trajectory 1120 and the second RCS trajectory1130. When the objects 1011 through 1013 are not ghost objects, thedifferences 1141 through 1143 fall within a range of calculated RCSdifferences. For example, because the object 1011 is located at theangle θ₁ with respect to a front side of the vehicle 100, a firstpredicted RCS value for the first beam pattern 710 and a secondpredicted RCS value for the second beam pattern 720 are calculated inadvance, and a difference between the first predicted RCS value and thesecond predicted RCS value is calculated in advance. When an actual RCSdifference for the object 1011 is calculated to be within a range ofpredicted RCS differences, the object 1011 is determined to be a realobject.

A clutter object, detected among the objects 1011 through 1013, isdetermined to be a real object based on the geometric information of theconfined space. In an example, when the confined space is a tunnel, theobjects 1011 and 1013 located at outermost positions with respect to afront side of the vehicle 100 are determined as clutter objects. Inanother example, when the confined space is a road with a barricade in acenter lane, the object 1011 is determined as a clutter object.

A target object among the objects 1011 through 1013 is determined to bea real object based on the clutter object. For example, when two clutterobjects exist, an object between the two clutter objects may bedetermined to be a target object. In an example, when the confined spaceis a tunnel, the object 1012 located between the objects 1011 and 1013determined as clutter objects is determined to be a target object. Inanother example, when the confined space is a road with a barricade in acenter lane, the objects 1012 and 1013, excluding the object 1011determined as a clutter object, are determined as target objects.

FIG. 12 is a flowchart illustrating an example of controlling a vehiclebased on a driving route.

Operations 1210 and 1220 of FIG. 12 are performed after operation 360 ofFIG. 3 is performed. Operations 1210 and 1220 are performed by, forexample, the object detection apparatus 200 described above withreference to FIGS. 2 through 11.

In operation 1210, the processor 220 generates a driving route for thevehicle 100 within the confined space based on detected clutter andtarget objects. For example, when the confined space is a tunnel, thevehicle 100 travels between side walls of the tunnel and the drivingroute is generated to avoid the target object.

In operation 1220, the processor 220 controls the vehicle 100 based onthe driving route. For example, driving devices of the vehicle 100 arecontrolled so that the vehicle 100 travels along the driving route.

FIG. 13 is a flowchart illustrating another example of an objectdetection method.

Operations 1310 through 1350 of FIG. 13 are performed by, for example,the object detection apparatus 200 described above with reference toFIGS. 2 through 12.

In operation 1310, the processor 220 acquires the geometric informationfor a confined space. For example, a location of the vehicle 100 isdetermined using a GPS. Based on the determined location, whether thevehicle 100 approaches the confined space or whether the vehicle 100 islocated in the confined space is determined. For example, when thevehicle 100 is determined to approach the confined space, the processor220 loads map data stored in the memory 230, to acquire the geometricinformation for the confined space. The acquired geometric informationis, for example, a floor plan of the confined space.

In operation 1320, the processor 220 sets a plurality of ROIs in theconfined space based on the geometric information, and a direction andthe location of the vehicle 100. The above description of FIGS. 4 and 5is similarly applicable to an example of setting a plurality of ROIs,and thus is not repeated here.

In operation 1330, the processor 220 determines a FoV for each of theplurality of ROIs. The above description of FIG. 6 is similarlyapplicable to an example of determining a FoV for each of a plurality ofROIs, and thus is not repeated here.

In operation 1340, the processor 220 emits a transmission signal todetect an object based on the determined FoV. For example, the processor220 calculates an orientation angle of each of a plurality of antennasto emit a transmission signal within the determined FoV, and emits thetransmission signal at the calculated orientation angle. A pattern ofthe transmission signal emitted by the transmitters at the calculatedorientation angle is referred to as a “beam pattern.” When an object islocated in a propagation path of the transmission signal, thetransmission signal is reflected from the object and returns to theobject detection apparatus 200. Reflected signals are received by thereceivers configured for the antennas.

In operation 1350, the processor 220 detects an object based on areceive signal. For example, the processor 220 calculates a distancefrom the object based on a difference between a time at which thetransmission signal is emitted and a time at which the receive signal isreceived, and calculates an angle of the object based on an angle atwhich the receive signal is received and an intensity of the receivesignal. Objects are detected for each of a plurality of ROIs. A processof detecting an object is performed in parallel for each of theplurality of ROIs.

The object detection apparatuses described herein and the operationsillustrated in FIGS. 1-13 improve the accuracy of the determination of avehicle's path in a confined space.

The object detection apparatus 200, the communicator 210, the processor220 and the memory 230 that perform the operations described in thisapplication are implemented by hardware components configured to performthe operations described in this application that are performed by thehardware components. Examples of hardware components that may be used toperform the operations described in this application where appropriateinclude controllers, sensors, generators, drivers, memories,comparators, arithmetic logic units, adders, subtractors, multipliers,dividers, integrators, and any other electronic components configured toperform the operations described in this application. In other examples,one or more of the hardware components that perform the operationsdescribed in this application are implemented by computing hardware, forexample, by one or more processors or computers. A processor or computermay be implemented by one or more processing elements, such as an arrayof logic gates, a controller and an arithmetic logic unit, a digitalsignal processor, a microcomputer, a programmable logic controller, afield-programmable gate array, a programmable logic array, amicroprocessor, or any other device or combination of devices that isconfigured to respond to and execute instructions in a defined manner toachieve a desired result. In one example, a processor or computerincludes, or is connected to, one or more memories storing instructionsor software that are executed by the processor or computer. Hardwarecomponents implemented by a processor or computer may executeinstructions or software, such as an operating system (OS) and one ormore software applications that run on the OS, to perform the operationsdescribed in this application. The hardware components may also access,manipulate, process, create, and store data in response to execution ofthe instructions or software. For simplicity, the singular term“processor” or “computer” may be used in the description of the examplesdescribed in this application, but in other examples multiple processorsor computers may be used, or a processor or computer may includemultiple processing elements, or multiple types of processing elements,or both. For example, a single hardware component or two or morehardware components may be implemented by a single processor, or two ormore processors, or a processor and a controller. One or more hardwarecomponents may be implemented by one or more processors, or a processorand a controller, and one or more other hardware components may beimplemented by one or more other processors, or another processor andanother controller. One or more processors, or a processor and acontroller, may implement a single hardware component, or two or morehardware components. A hardware component may have any one or more ofdifferent processing configurations, examples of which include a singleprocessor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 3-13 that perform the operationsdescribed in this application are performed by computing hardware, forexample, by one or more processors or computers, implemented asdescribed above executing instructions or software to perform theoperations described in this application that are performed by themethods. For example, a single operation or two or more operations maybe performed by a single processor, or two or more processors, or aprocessor and a controller. One or more operations may be performed byone or more processors, or a processor and a controller, and one or moreother operations may be performed by one or more other processors, oranother processor and another controller. One or more processors, or aprocessor and a controller, may perform a single operation, or two ormore operations.

Instructions or software to control computing hardware, for example, oneor more processors or computers, to implement the hardware componentsand perform the methods as described above may be written as computerprograms, code segments, instructions or any combination thereof, forindividually or collectively instructing or configuring the one or moreprocessors or computers to operate as a machine or special-purposecomputer to perform the operations that are performed by the hardwarecomponents and the methods as described above. In one example, theinstructions or software include machine code that is directly executedby the one or more processors or computers, such as machine codeproduced by a compiler. In another example, the instructions or softwareincludes higher-level code that is executed by the one or moreprocessors or computer using an interpreter. The instructions orsoftware may be written using any programming language based on theblock diagrams and the flow charts illustrated in the drawings and thecorresponding descriptions in the specification, which disclosealgorithms for performing the operations that are performed by thehardware components and the methods as described above.

The instructions or software to control computing hardware, for example,one or more processors or computers, to implement the hardwarecomponents and perform the methods as described above, and anyassociated data, data files, and data structures, may be recorded,stored, or fixed in or on one or more non-transitory computer-readablestorage media. Examples of a non-transitory computer-readable storagemedium include read-only memory (ROM), random-access memory (RAM), flashmemory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs,DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, magnetictapes, floppy disks, magneto-optical data storage devices, optical datastorage devices, hard disks, solid-state disks, and any other devicethat is configured to store the instructions or software and anyassociated data, data files, and data structures in a non-transitorymanner and provide the instructions or software and any associated data,data files, and data structures to one or more processors or computersso that the one or more processors or computers can execute theinstructions. In one example, the instructions or software and anyassociated data, data files, and data structures are distributed overnetwork-coupled computer systems so that the instructions and softwareand any associated data, data files, and data structures are stored,accessed, and executed in a distributed fashion by the one or moreprocessors or computers.

While this disclosure includes specific examples, it will be apparentafter an understanding of the disclosure of this application thatvarious changes in form and details may be made in these exampleswithout departing from the spirit and scope of the claims and theirequivalents. The examples described herein are to be considered in adescriptive sense only, and not for purposes of limitation. Descriptionsof features or aspects in each example are to be considered as beingapplicable to similar features or aspects in other examples. Suitableresults may be achieved if the described techniques are performed in adifferent order, and/or if components in a described system,architecture, device, or circuit are combined in a different manner,and/or replaced or supplemented by other components or theirequivalents. Therefore, the scope of the disclosure is defined not bythe detailed description, but by the claims and their equivalents, andall variations within the scope of the claims and their equivalents areto be construed as being included in the disclosure.

What is claimed is:
 1. A processor implemented object detection methodfor a vehicle traveling in a confined space, comprising: determining afirst beam pattern and a second beam pattern based on geometricinformation of the confined space; detecting first candidate objectsbased on a first transmission signal emitted to form the first beampattern using at least one antenna; detecting second candidate objectsbased on a second transmission signal emitted to form the second beampattern, which is different from the first beam pattern, using the atleast one antenna; detecting at least one clutter object based on thefirst candidate objects and the second candidate objects; detecting atarget object based on the at least one clutter object; and controllingan operation of the vehicle based on a result of the detecting of thetarget object.
 2. The method of claim 1, wherein the detecting of thefirst candidate objects comprises: emitting the first transmissionsignal to form the first beam pattern using the at least one antenna;receiving a first receive signal corresponding to the first transmissionsignal; detecting objects for the first receive signal based on thefirst receive signal; and classifying the objects for the first receivesignal into a plurality of preset regions of interest (ROIs) based on adistance from the vehicle, the plurality of ROI comprising a targetregion, and the first candidate objects being included in the targetregion.
 3. The method of claim 2, wherein the detecting of the secondcandidate objects comprises: emitting the second transmission signal toform the second beam pattern using the at least one antenna; receiving asecond receive signal corresponding to the second transmission signal;detecting objects for the second receive signal based on the secondreceive signal; and classifying the objects for the second receivesignal into the plurality of ROIs based on the distance from thevehicle, and the target region comprising the second candidate objects.4. The method of claim 3, wherein the detecting of the at least oneclutter object comprises detecting the at least one clutter object basedon a radar cross-section (RCS) of each of the first candidate objectsand an RCS of each of the second candidate objects.
 5. The method ofclaim 1, wherein the detecting of the target object comprises, inresponse to two clutter objects being detected, detecting an objectlocated between the two clutter objects as the target object.
 6. Themethod of claim 1, wherein the determining of the first beam pattern andthe second beam pattern comprises: setting a plurality of ROIs in theconfined space based on the geometric information and a location of thevehicle; and determining the first beam pattern and the second beampattern that cover the plurality of ROIs.
 7. The method of claim 1,further comprising: acquiring the geometric information based on alocation of the vehicle.
 8. The method of claim 1, further comprising:generating a driving route for the vehicle within the confined spacebased on the at least one clutter object and the target object; andcontrolling the vehicle based on the driving route.
 9. A non-transitorycomputer-readable storage medium storing instructions that, whenexecuted by a processor, cause the processor to perform the method ofclaim
 1. 10. An object detection apparatus included in a vehicletraveling in a confined space, comprising: a processor configured to;determine a first beam pattern and a second beam pattern based ongeometric information about the confined space; detect first candidateobjects based on a first transmission signal emitted to form the firstbeam pattern using at least one antenna; detect second candidate objectsbased on a second transmission signal emitted to form the second beampattern, which is different from the first beam pattern, using the atleast one antenna; detect at least one clutter object based on the firstcandidate objects and the second candidate objects; detect a targetobject based on the at least one clutter object; and control anoperation of the vehicle based on a result of the detection of thetarget object.
 11. The object detection apparatus of claim 10, furthercomprising a memory configured to store instructions, wherein theprocessor is further configured to execute the instructions to configurethe processor to: determine the first beam pattern and the second beampattern based on geometric information about the confined space; detectthe first candidate objects based on the first transmission signalemitted to form the first beam pattern using the at least one antenna;detect the second candidate objects based on the second transmissionsignal emitted to form the second beam pattern using the at least oneantenna; detect the at least one clutter object based on the firstcandidate objects and the second candidate objects; and detect thetarget object based on the at least one clutter object.
 12. The objectdetection apparatus of claim 10, wherein to perform the detecting of thefirst candidate objects, the processor is configured to: emit the firsttransmission signal to form the first beam pattern using the at leastone antenna; receive a first receive signal corresponding to the firsttransmission signal; detect objects for the first receive signal basedon the first receive signal; and classify the objects for the firstreceive signal into a plurality of preset regions of interest (ROIs)based on a distance from the vehicle, the plurality of ROIs comprising atarget region, and the first candidate objects being included in thetarget region.
 13. The object detection apparatus of claim 12, whereinto perform the detecting of the second candidate objects, the processoris configured to: emit the second transmission signal to form the secondbeam pattern using the at least one antenna; receive a second receivesignal corresponding to the second transmission signal; detect objectsfor the second receive signal based on the second receive signal; andclassify the objects for the second receive signal into the plurality ofROIs based on the distance from the vehicle, and the target regioncomprising the second candidate objects.
 14. The object detectionapparatus of claim 13, wherein to perform the detecting of the at leastone clutter object, the processor is configured to detect the at leastone clutter object based on a radar cross-section (RCS) of each of thefirst candidate objects and an RCS of each of the second candidateobjects.
 15. The object detection apparatus of claim 10, wherein toperform the detecting of the target object, the processor is configuredto, in response to two clutter objects being detected, detect an objectlocated between the two clutter objects as the target object.
 16. Theobject detection apparatus of claim 10, wherein to perform thedetermining of the first beam pattern and the second beam pattern, theprocessor is configured to: set a plurality of ROIs in the confinedspace based on the geometric information and a location of the vehicle;and determine the first beam pattern and the second beam pattern thatcover the plurality of ROIs.
 17. The object detection apparatus of claim10, wherein the program is further executed to acquire the geometricinformation based on a location of the vehicle.
 18. The object detectionapparatus of claim 10, wherein the program is further executed to:generate a driving route for the vehicle within the confined space basedon the at least one clutter object and the target object; and controlthe vehicle based on the driving route.
 19. The object detectionapparatus of claim 10, wherein the confined space comprises at least oneof a tunnel and a road with a barricade.
 20. An object detection methodfor a vehicle traveling in a confined space, method comprising:acquiring geometric information of the confined space; setting aplurality of regions of interest (ROIs) in the confined space based onthe geometric information and a determined location of the vehicle;determining a field of view (FoV) for each of the plurality of ROIs;calculating an orientation angle of each of a plurality of antennaswithin the determined FoV, emitting a transmission signal based at thecalculated orientation angle; detecting an object based on a receivesignal corresponding to the transmission signal; and controlling anoperation of the vehicle based on a result of the detecting of theobject in the confined space.
 21. A processor implemented objectdetection method for a vehicle traveling in a confined space,comprising: determining a plurality of regions of interest (ROIs) basedon geometric information of the confined space; determining beampatterns based on the plurality of ROIs; detecting correspondingcandidate objects based on transmission signals emitted to form the beampatterns using at least one antenna; detecting clutter objects based oncurrently detected candidate objects and subsequently detected candidateobjects; and detecting a target object based on the clutter objects inthe confined space; and controlling an operation of the vehicle based ona result of the detecting of the target object in the confined space.22. The method of claim 21, wherein the determining of the plurality ofROIs is further based a determined current direction and a determinedlocation of the vehicle.
 23. The method of claim 22, wherein the clutterobjects are detected based on a radar cross-section (RCS) of thecandidate objects.
 24. The method of claim 23, further comprising:generating a driving route for the vehicle within the confined spacebased on the clutter objects and the target object; and controlling thevehicle based on the driving route.